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Robustness in signal processing is crucial for the purpose of reliably interpreting physiological features from noisy data in biomedical applications. We present a robust algorithm based on the reformulation of a well-known spatial filtering and feature extraction algorithm named Common Spatial Patterns (CSP). We cast the problem of learning CSP into a probabilistic framework, which allows us to gain...
Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results...
Extraction of relevant features that capture the invariant characteristics specific to each brain state is very important in order to implement a suitable brain-computer interface (BCI) system. This paper presents an algorithm called one-versus-the-rest (OVR), which is an extension of a well-known method called common spatial patterns (CSP) to multi-class case, to extract signal components specific...
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